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Found 11 papers, 3 papers with code
Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content.
ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration
Mixture-of-Experts (MoE) Transformer, the backbone architecture of multiple phenomenal language models, leverages sparsity by activating only a fraction of model parameters for each input token.
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.
BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks
To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.
SWNet: Small-World Neural Networks and Rapid Convergence
This transformation leverages our important observation that for a set level of accuracy, convergence is fastest when network topology reaches the boundary of a Small-World Network.
AgileNet: Lightweight Dictionary-based Few-shot Learning
The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time.
DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
The resulting models are therefore considered to be the IP of the model builder and need to be protected to preserve the owner's competitive advantage.
Cryptography and Security
Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks
We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model.
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning
Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems.
DeepSecure: Scalable Provably-Secure Deep Learning
This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting.
Cryptography and Security
